Multiple Classifiers to verify the Online Signature

Nowadays biometric increasingly used in many applications that has strong relation to our live; it's a reliable mean as an alternative to the traditional methods of personal identification. As a behavioral biometric, an online signature still has some shortcomings because of that nature. Furthermore, features in online signature verification system can be either global or local; the techniques that can be used also variety. In this paper both global and local features were used. To classify the mentioned features; the back-propagation neural network (BPNN) technique was used to classify the local features, whereas, the global features was classified by the probabilistic model. Once the results obtained from the local classifier and global classifier, the “AND” fusion was used to combine the two classifiers for final decision. SVC2004 dataset was used to evaluate the proposed method in term of False Rejection Rate (FRR) and False Acceptance Rate (FAR). The obtained results for FRR and FAR were 0.3% and 0.5% respectively. These results are encouraging when compared with related existing studies. KeywordsOnline Signature; Probabilistic Modeling; Back-propagation Neural Network (BPNN).

[1]  Réjean Plamondon,et al.  Automatic signature verification and writer identification - the state of the art , 1989, Pattern Recognit..

[2]  Loris Nanni,et al.  An On-Line Signature Verification System Based on Fusion of Local and Global Information , 2005, AVBPA.

[3]  Trevor Hastie,et al.  A model for signature verification , 1991, Conference Proceedings 1991 IEEE International Conference on Systems, Man, and Cybernetics.

[4]  Jianying Hu,et al.  A Hidden Markov Model approach to online handwritten signature verification , 1998, International Journal on Document Analysis and Recognition.

[5]  Pietro Perona,et al.  Visual Identification by Signature Tracking , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Vishvjit S. Nalwa,et al.  Automatic On-line Signature Verification , 1997 .

[7]  Berrin A. Yanikoglu,et al.  Biometric Authentication Using Online Signatures , 2004, ISCIS.

[8]  Hyotaek Lim,et al.  An Efficient Online Signature Verification Scheme Using Dynamic Programming of String Matching , 2011, ICHIT.

[9]  Sukhan Lee,et al.  Offline tracing and representation of signatures , 1992, IEEE Trans. Syst. Man Cybern..

[10]  Robert Sabourin,et al.  An extended-shadow-code based approach for off-line signature verification , 1993, Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR '93).

[11]  Marzuki Khalid,et al.  Online Signature Verification with Neural Networks Classifier and Fuzzy Inference , 2009, 2009 Third Asia International Conference on Modelling & Simulation.

[12]  Alisher Kholmatov,et al.  Biometric identity verification using on-line & off-line signature verification , 2003 .

[13]  Bernhard Sick,et al.  Online Signature Verification With Support Vector Machines Based on LCSS Kernel Functions , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[14]  Sargur N. Srihari,et al.  Offline Signature Verification And Identification Using Distance Statistics , 2004, Int. J. Pattern Recognit. Artif. Intell..

[15]  Anil K. Jain,et al.  On-line signature verification, , 2002, Pattern Recognit..

[16]  Changping Liu,et al.  On-line signature verification using local shape analysis , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..